Abstract: In mobile ad hoc network, Position aided routing protocols can offer a significant performance increase over
traditional ad hoc routing protocols. As position information is broadcasted including the enemy to receive.
Routes may be disconnected due to dynamic movement of nodes. Such networks are more vulnerable to both
internal and external attacks due to presence of adversarial nodes. These nodes affect the performance of routing
protocol in ad hoc networks. So it is essential to identify the neighbours in MANET. The "Neighbor Position
Verification" (NPV), is a routing protocol designed to protect the network from adversary nodes by verifying
the position of neighbor nodes to improve security, efficiency and performance in MANET routing.

A Review On Job Shop Scheduling Using Non-Conventional Optimization Algorithm

Abstract: A great deal of research has been focused on solving job shop scheduling problem (∫J), over the last four decades, resulting in a wide variety of approaches. Recently much effort has been concentrated on hybrid methods to solve ∫J, as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that lead to combinatorial optimization methods and a meta-strategy which guides the search out of local optima. In this paper, authors seek to assess the work done in the job-shop domain by providing a review of many of the techniques used. It is established that Non- conventional optimization methods should be considered complementary rather than competitive. In addition, this work suggests guide-lines on features that should incorporated to create a good ∫J system. Finally, the possible direction for future work is highlighted so that current barriers within ∫J may be surmounted as researchers approach in the 21st century.

Microcontroller Based Current Detection In Ac Locomotives

Abstract: An electric locomotive is a locomotive powered by electricity from overhead lines, a third rail or an on-board energy storage device.During the course of run of the locos, there arise situations that the motor consumes current that is above the maximum or below the minimum ratings. This phenomenon causes flashover, burning out of contactors etc. If we could prevent these undesirable anomalies a lot of money can be saved and also the maintenance works gets reduced.This necessitates the development of a system that helps us in detecting the unusual current consumption so that all the undesirable anomalies can be avoided. Presently there are no such systems that help us in detecting this problem. This paper aims in developing such a system using a microcontroller that digitally notifies the loco pilot for any unusual current consumption so that if such a situation should arise the specific faulty motor can be isolated before it gets damaged.

Investigation of Effect of Operating Parameters of A CNC Cylindrical Grinding Machine on Geometric Dimensioning and Tolerancing

Abstract: Machining processes are met with dimensional and geometrical variations in a product during machining operation. The amount of variation needs to be more strictly defined for accurately machined parts. Geometric dimensioning and tolerancing (GD&T) definition provides the precision required for allowing manufacturing of most economical parts. Crankshaft flange is required to be machined with higher degree of precision. If geometrical accuracies are not met the crankshaft-flywheel assembly will cause wear, unbalance and vibration, leading to poor functionality. The face of crankshaft flange is evaluated for geometric tolerances- flatness and runout. A two level three factor factorial model is designed and analyzed on Minitab 16 software to identify the most affecting machining parameter among speed, feed and depth of cut on face flatness and face runout.

Classification of Brain Tumor Using Support Vector Machine Classfiers

Abstract: Magnetic resonance imagi ng (MRI) is an imaging technique that has played an important role in neuro science research for studying brain images. Classification is an important part in order to distinguish between normal patients and those who have the possibility of having abnormalities or tumor. The proposed method consists of two stages: feature extraction and classification. In first stage features are extracted from images using GLCM. In the next stage, extracted features are fed as input to Kernel-Based SVM classifier. It classifies the images between normal and abnormal along with Grade of tumor depending upon features. For Brain MRI images; features extracted with GLCM gives 98% accuracy with Kernel-Based SVM Classifiesr. Software used is MATLAB R2011a.

Performance Analysis of Contourlet Features with SVM Classifier for the Characterization of Atheromatous Plaque in Intravascular Ultrasound Images

Abstract: Medical Image Processing has full-fledged in recent years and demands high accuracy since it deals with human creature. Artificial intelligent is one of the techniques used in this field which aims to reduce human error as much as possible. Hence, in this work, the local characterization of atheromatous plaque is proposed using the feature vector which includes the texture features extracted from the sub bands of third level contourlet transform. The extracted feature vectors are inputted to the SVM Classifier. The classifier differentiates each pixel in the IVUS image as Fibrotic, Lipidic and Calcified plaque tissues. The pixel based classification performance is assessed in terms of sensitivity, specificity and accuracy. The time taken to obtain the average accuracy of 95.92% is about 2 seconds under testing condition.

Abstract: This paper describes with an idea of how the automatic recovery paradigm will work for audio/video streaming data packets in Wireless Sensor Networks. In recent works observed that compressed sensing theory can obtain all the signal information from far fewer measurements by means of non-adaptive linear projection, and can recover the signal information using non-linear reconstruction technique. In this paper according to the compressive sensing theory, a new video codec system has been developed. In the encoding process, the audio/video frame sequences are divided into groups, each group include intra and inter frames. A random measurement matrix is constructed to measure different frames. Then the measurements are quantized, the quantization codes are transmitted on the channel. In decoding process, each frame sequence is reconstructed using the St OMP algorithm and processed in the present system then experimental results shown that the proposed method exhibits better results over the traditional video codec with keeping the same quality of the video image, and it can reduce sampling number significantly, realize easily, encode/decode more efficiently.